We find that models rely more on decomposition-based reasoning! Using the same metrics we propose in Lanham et al., we conclude that models change their answers more when they are forced to answer with a truncated or corrupted version of their decomposition-based reasoning.
PROMPT ENGINEERING
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Improving Model Reasoning Through Question Decomposition Methods
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To improve the faithfulness of model-generated reasoning, we study two other ways of eliciting reasoning from models in Radhakrishnan et al. These methods rely on question decomposition, or breaking down a question into smaller subquestions to help answer the original question.
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Factored vs Chain-of-Thought Decomposition in Prompt Engineering
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Factored decomposition prompts the model to generate subquestions, but has the model answer subquestions in separate contexts. Chain-of-thought decomposition also has the model generate subquestions, but answers all of them in a single context, like chain-of-thought prompting.
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Testing Chain of Thought Reasoning Faithfulness in AI Models
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We make edits to the model’s chain of thought (CoT) reasoning to test hypotheses about how CoT reasoning may be unfaithful. For example, the model’s final answer should change when we introduce a mistake during CoT generation.
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Chain of Thought Impact on Model Reasoning Accuracy
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For some tasks, forcing the model to answer with only a truncated version of its chain of thought often causes it to come to a different answer, indicating that the CoT isn’t just a rationalization. The same is true when we introduce mistakes into the CoT.
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Do AIs Understand Logic Puzzles? Reasoning Test
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Understand logic puzzles "Respond to this question only based on the information provided here. Cats like dogs, and dogs like rabbits. Cats like anything that dogs like. I really really dislike rabbits. How do cats feel about rabbits?" https://replicate.com/p/fqch35bbyrcr j7zhl7zxb5k6ha
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AI Content Strategy and LLM Performance: Claude 2 vs Google Bard
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00:04:46 — AI-Powered Content Strategy Driving 100k+ Podcast Downloads 00:26:08 — Claude 2 Impresses in Content Summarization Experiment 00:36:36 — Google Bard Updates (but it still underperforms) 00:40:59 — Cassie Kozyrkov, Chief Decision Scientist at Google, headlines MAICON
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Podcast Growth Strategy: From 5K to 100K Downloads with AI
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We kick off this week’s podcast with the inside story of how we went from less than 5,000 podcast downloads in all of 2022, to 100,000+ YTD in 2023. A mix of content strategy, consistency, AI tools, and luck. https://
marketingaiinstitute.com/blog/the-marke
ting-ai-show-episode-55-ai-powered-content-strategy-claude-2-from-anthropic-and-major-google-bard-updates
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Salesforce Launches Prompt Studio for Generative AI Experience
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#Salesforce #GenerativeAI https://
asagarwal.com/salesforces-re
volutionary-leap-introducing-prompt-studio-to-amplify-your-generative-ai-experience/
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Promote God Mode ChatGPT Prompt Bible for productivity boost
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If you're reading this far, then you should check out my God Mode ChatGPT Prompt Bible with copy & paste prompts to 10x your productivity: http://
godofprompt.ai/god-mode-chatg
pt
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